# A Quick Start of cola Package

Author: Zuguang Gu ( z.gu@dkfz.de )

Date: 2020-01-01

Package version: 1.2.1

Assume your matrix is stored in an object called `mat`, to perform consensus partitioning with cola, you only need to run following code:

    # code only for demonstration
    mat = adjust_matrix(mat)  # optional
    rl = run_all_consensus_partition_methods(mat, mc.cores = ...)
    cola_report(rl, output_dir = ..., mc.cores = ...)

In above code, there are three steps:

1. Adjust the matrix. In this step, rows with too many `NA`s are removed. Rows with very low variance are removed. `NA` values are imputed if there are not too many in each row. Outliers are adjusted in each row. This step is partition methods are `hclust` (hierarchical clustering with cutree), `kmeans` (k-means clustering), `skmeans::skmeans` (spherical k-means clustering), `cluster::pam` (partitioning around medoids clustering) and `Mclust::mclust` (model-based clustering). The default methods to extract top n rows are `SD` (standard deviation), `CV` (coefficient of variation), `MAD` (median absolute deviation) and `ATC` (ability to correlate to other rows).
2. Generate a detailed HTML report for the complete analysis.

There are examples on real datasets for cola analysis that can be found at https://jokergoo.github.io/cola_collection/.